A NEW CONTROL STRATEGY TO IMPROVE THE GROUND SOURCE HEATPUMP’S EFFICIENCY WITH A RECYCLED PRODUCT

Amir Rezaei-Bazkiaei∗Department of Civil , Structural& Environmental Engineering

University at BuffaloBuffalo, NY-14260

Email: ar92@buffalo.edu

Ehsan Dehghan-NiriSmart Structures Research LabDepartment of Civil, Structural& Environmental Engineering

University at BuffaloBuffalo, NY 14260

Email: ehsandeh@buffalo.edu

Ebrahim M. KolahdouzDepartment of Mechanical& Aerospace Engineering

University at BuffaloBuffalo, NY-14260

Email: mkolahdo@buffalo.edu

Gary F. DargushDepartment of Mechanical& Aerospace Engineering

University at BuffaloBuffalo, NY-14260

Email: gdargush@buffalo.edu

A. Scott WeberDepartment of Civil , Structural& Environmental Engineering

University at BuffaloBuffalo, NY-14260

Email: sweber@buffalo.edu

ABSTRACTDespite numerous efforts to impose control measures on the

heat pump side of Ground Source Heat Pump (GSHP) systems,there has been little thought into the potential of control on theground characteristics. This is perhaps because of a predisposi-tion to believe that ground related works usually are associatedwith extra capital investment that makes any modification lessfavorable than making changes to the heat pump unit. An ef-fective control strategy with a non-homogeneous soil profile forthe ground side of horizontal GSHPs was investigated in this re-search. The model incorporates the effects of a variety of surfaceenergy fluxes to provide an accurate estimate of the ground ther-mal regime. The developed model was utilized successfully inconjunction with the MATLAB Genetic Algorithm (GA) tool-box to obtain the optimized operational parameters for a GSHPin a cold climate condition (Buffalo, NY). A properly sized andengineered non-homogeneous soil profile demonstrated the po-tential to boost the capacity of GSHP systems to a significant

∗Address all correspondence to this author.

level. The potential benefits of a recycled product of tire indus-try, Tire Derived Aggregate (TDA), as an insulation blanket wasassessed via the optimization algorithm. TDA was demonstratedto be effective in heating mode in a cold climate environment byincreasing the energy extraction rates from the ground by about15% annual. The annual percentage increase in energy dissipa-tion rate to the ground, in cooling season, with TDA blanket was7.6%. The results are suggestive of the beneficial application of alayered system to increase the performance of GSHPs. A shift indesign perspective toward consideration of more control strate-gies for the ground pipe side of GSHPs is suggested based on themodel results.

INTRODUCTIONHeat pump sizing starts with the careful estimation of the

heating/cooling loads for the prospect building via acceptedmethods such as the bin method. The bin method is based ona full survey of the total number of hours in a design year that a

1 Copyright c© 2012 by ASME

Proceedings of the ASME 2012 International Mechanical Engineering Congress & Exposition IMECE2012

November 9-15, 2012, Houston, Texas, USA

IMECE2012-93086

certain air temperature range (bin) occurs. The heating/coolingbudget analysis forms the base for the peak load GSHP design.It is noteworthy that methods like bin method do not take intoaccount the sequence or real-time occurrence of the peak loaddemand in time. In other words, the question is the duration ofa worst case load condition rather than when and for how longthe load is sustained. Therefore, it does not matter in such de-sign procedures what the climate and/or source condition is be-fore or after the worst case condition, a matter that can influencethe overall performance of the system. Moreover, these methodsusually assume a constant source/sink strength (ground temper-ature) based on the maximum and minimum observed data orsemi-experimental formulas [1] (equations 1 and 2) which againdo not reflect on the dynamic variation of the boundary condi-tions and the time dependent nature of the system’s function-ality. The inherent tendency of the traditional design methodsto stray from the path of optimized operation has made the dy-namic, real-time analysis of GSHPs the focus of several recentresearch studies [2, 3]. The following design steps after the heatbudget analysis involve ground pipe sizing and heat pump selec-tion.

Tf ,ih = Tf ,imin +Tf ,imean −Tf ,imin

Tairmean −Tairmin(Tair −Tairmax) (1)

Tf ,ic = Tf ,imean +Tf ,imax −Tf ,imean

Tairmax −Tairmean(Tair −Tairmean) (2)

where Tf ,ih : entering water temperature to the ground inheating mode, Tf ,ic : entering water temperature to the groundin cooling mode, Tf ,imin : minimum design entering water tem-perature in heating, Tf ,imean : average design entering water tem-perature in heating or cooling, Tf ,imax : maximum design enteringwater temperature in cooling, Tairmean: average annual ambientair temperature, Tairmin: minimum annual ambient air tempera-ture, Tairmax: maximum annual ambient air temperature, Tair: airtemperature at the time of simulation

With the ever increasing need for preserving energy, the ideaof utilizing capacity control strategies for GSHPs was initiatedby researchers about two decades ago [4]. The capacity con-trol practices deal with the most feasible engineering optionsto meet dynamically the building load requirements and mini-mize the waste energy in the heating/conditioning process. AsMadani summarizes [5], capacity control usually involves eitherthe control on the components of a GSHP system (e.g. the com-pressor, condenser, ground pipes, etc.) or a change in designconfigurations for different seasons or advanced control algo-rithms. The complex nature of the interplay between the unitsof a GSHP suggests that changes in operational parameters of

one unit can affect the overall performance of the system. Fromthe modeling point of view, researchers have tried to tackle thesecomplexities by dynamically modeling the interactions betweenthese units via packaged softwares such as TRNSYS [2, 6–10]or in combination with thermodynamic data base models such asEES [3] or by self developed programs [11] which are capable ofmodeling the dynamic interplay between these units. The gen-eral structure of such modeling efforts often consists of separatefirst-level models (e.g. ground pipe and heat pump) which eachhave sub-models of second-level (e.g. compressor, evaporator,etc.) with higher details. The functional relationship betweenthe sub-models is constructed via operational parameters such aswater/brine and refrigerant flow rates. The level of complexityof these sub-models significantly depends on the purpose of themodeling effort as described by Madani [5].

The focus of this paper is on the analysis of the system effi-ciency and optimization via control on the source/sink (ground)side with a secondary focus on the heat pump unit. Simple semi-empirical equations for the entering water temperature to thepump in heating and cooling seasons, described in Eqn. (1) andEqn. (2) respectively, were adopted from the international groundsource heat pump association (IGSHPA) design manual [1] tomake the link between the ground pipe and the heat pump. Theground pipe model, on the other hand, is an elaborate version ofa surface energy balance model, which unlike thermal resistancemethod, is capable of solving for the temperature distribution ofthe entire soil profile to obtain the outlet water temperature fromthe ground pipes. A detailed description of the surface energybalance equations, parameters and the solution methods is pre-sented in [12]. A summary of the heat fluxes is listed in the ’sur-face energies’ section. Our motivation behind the developmentof this ground model arises from the desire to investigate the po-tential benefits of a non-homogeneous soil profile on the groundpipe performance. The developed model was employed to inves-tigate the effectiveness of utilization of a recycled product of tireindustry as an insulation material above the burial depth of theground pipes.

Tire Derived Aggregate (TDA) is the proposed insula-tion material to be used as the intermediate layer in the non-homogeneous model. TDA mainly consists of chopped pieces ofused tires in a variety of nominal sizes ranging from 1 to above5 inches [13]. The idea of employment of TDA, also referredto as tire chips, tire shreds, and tire mulch, in civil engineeringapplications was first initiated by Humphrey [14]. New applica-tions for TDA, mostly in civil engineering, have been proposedbased on its unique physical characteristics. TDA’s relatively lowdensity compared to conventional backfill makes it a viable alter-native fill material where a lighter fill material is desired in con-struction [15]. Its low thermal conductivity on the other hand,provoked the thought of utilizing TDA as an alternative insula-tion material in road base insulation and some agricultural ap-plications to modulate temperature fluctuation on the soil sur-

2 Copyright c© 2012 by ASME

face [13, 14, 16]. There are only a few studies in the literaturethat focus on the properties of TDA measuring physical proper-ties [16], compaction densities for different sizes of chips [13],and thermal properties of tire chip samples [13, 17, 18]. A listof thermal properties and density of tire shreds reported in thesestudies is presented in Table 1. A thermal conductivity value of0.29 Wm−1◦C−1, specific heat of 500 JKg−1◦C−1 and densityof 720 Kgm−3 were chosen as representative properties of TDAmaterial for the purpose of the analyses in this paper.

TABLE 1. TDA thermal and physical properties in literature

Material Thermal conductivity Specific heat Density

(Wm−1◦C−1) (JKg−1◦C−1) (Kgm−3)

Tire chips [13] 0.149-0.164 NA 513

Tire chips [16] NA NA 1060-1100

Tire chips [17] 0.29 1.15 720

Tire shred [18] 0.564 507 641

MATERIAL AND METHODSA brief summary of the surface energy balance equations

are first introduced. The second subsection describes the groundpipe specifications and also the method pipes are modeled fromthe physical domain into the numerical domain. Numerical solu-tion of the ground pipe model and the parameter settings for thegenetic algorithm are described in the following sections.

Surface energiesThe surface boundary condition takes into account the ef-

fects of energy balance due to variety of mechanisms responsiblefor surface-ambient heat interaction. The total energy balance onthe ground surface (Qt ,Watts) can be written as [19, 20]:

Qt = Qc +Qe +Qh +Qle +Qli +Qsi +Qp (3)

Qc= Conduction heat flux through snow layer or ground surface(Watts)

Qe= Turbulent exchange of latent heat (Watts)

Qh= Turbulent exchange of sensible heat (Watts)

Qle= Emitted long wave radiation heat flux (Watts)

Qli= Incoming long wave radiation (Watts)

Qsi= Solar radiation reaching the surface of earth (Watts)

Qp= Heat flux due to precipitation (Watts)

For a detailed description of each heat flux input parametersand formulas refer to [12].

Ground pipe modelingThe ground pipe configuration consists of a horizontal pipe

of total length of 61 meters (L) and 34 inch diameter, 3 m dis-

tance between inlet and outlet, buried at the depth of 2 m ( jpipe),with the working fluid flow rate of 3 gallons per minute (0.19kg/s). Assuming there is no thermal interaction between pipes,the solution domain (Figure 1) was considered to be from thecenter line of the pipe to the mid-span of the distance betweenpipes (1.5 m), in the x-direction, and from ground surface to thefarfield (5 m) in the y-direction. Soil thermal conductivity (Ks)and thermal diffusivity (αs) were selected based on the soil androck classification guideline [21] to be equal to 1.67 Wm−1◦K−1

and 66 ·10−8 m2s−1, respectively.

FIGURE 1. Solution domain discretization and TDA configuration[12]

To account for the three-dimensional behavior of the pipeand the surrounding soil, the effect of the working fluid flow ratewas considered along the pipe direction. The third dimension ofthe problem was modeled by splitting the physical domain in thepipe direction into a series of cross sections (slices) of the soil

3 Copyright c© 2012 by ASME

profile for each time step, including the nodal temperature of thefluid at the pipe’s location. Figure 2 shows how the slices arespaced in the pipe direction to cover the temperature distributionof the entire 3D domain. At each time step, the nodal tempera-tures of each cross section were obtained and subsequently up-dated for the next slice along the pipe’s length via Eqn. (4) toachieve a temperature distribution of soil and fluid at the end ofthe pipe (L). The same process was repeated for the next timesteps until the end of the simulation time. A schematic represen-tation of the cross sections was depicted earlier Figure 1. Referto [12] for a detailed description of the numerical solution meth-ods.

Tf ,out = Ts − (Ts −Tf ,i)e−KsLmCp, f (4)

where α is the thermal diffusivity of the medium through whichheat travels, Tf ,out is the fluid temperature exiting a pipe of lengthL (m). Ts is the surrounding soil temperature, Tf ,i is the initialwater temperature entering the pipe, and Ks is the soil thermalconductivity (Wm−1◦C−1). m is the mass flow rate (Kgs−1) andCp, f is the specific heat of the working fluid (Jkg−1 ◦C−1).

FIGURE 2. Schematic of slices of 3D domain in pipe directions [12]

GENETIC ALGORITHMThe idea of utilization of Genetic Algorithm (GA) in engi-

neering applications was inspired by natural selection. This se-lection represents the concept of the higher survival chance ofthe fittest individual in its environment through successive gen-erations to find the optimal design parameters among the others.More information about GA optimization can be found in [22].The main advantages of GA over traditional optimization algo-rithms are that it does not require and does not depend on gradi-

ent information of the objective function; instead it uses a pop-ulation of design points and randomly utilizes information fromeach generation to the subsequent one; and, there is a potentialto make the population converge to the Pareto-optimal set. Thelatter property is a crucial appeal of GA in multi-objective op-timization problems that enables us to use GA in combinationwith a Pareto-set filter [23] to obtain a near approximation of theentire set of non-dominated solutions. The specifications of theGA implementation in the present study are listed in Table 2.

TABLE 2. Gaoptimset options used in the analysisPopulationSize 500 CreationFcn @gacreationuniform

Generations 50 FitnessScalingFcn @fitscalingprop

PopulationType doubleVector SelectionFcn @selectiontournament

EliteCount 2 CrossoverFcn @crossoversinglepoint

CrossoverFraction 0.7 MutationFcn (@mutationuniform,.02)

MigrationDirection forward Display ’iter’

MigrationInterval 15 UseParallel ’always’

MigrationFraction 0.1

Given the complex nature of the relationships between aGSHP unit, designers often should heavily rely on their level ofexperience to select the best heat pump design. Even with a hugedeal of experience, these designs are potentially distant from therequired heating loads of a building due to the common practicepeak load design criteria. Several researchers in the heat transferliterature have come to the conclusion that optimization schemescan benefit the design process where the effects of the climaticconditions is to be closely considered in an efficient design pro-cess [24–29]. In their comprehensive work on vertical [27] andhorizontal [28] ground source heat pumps, Sanaye modeled thethermodynamic cycle of the heat pump in conjunction with thethermal resistance pipe model to obtain the optimized operationalparameters. Sayyaadi does a similar study via exergy analysis ofa vertical ground source heat pump [29]. Authors of this paperare specifically concerned with the effects of the climatic con-ditions on the selection of the optimized operational parametersfor a horizontal GSHP in the presence of a non-homogeneoussoil profile. The optimization algorithm is supposed to providethe designers of the system with beneficial information regard-ing the ground pipe design where the absence of the experiencecan cause a wide deviation from the optimized cost and operationpath.

To evaluate the potential benefits of the intermediate layerin a cold climate, Buffalo weather conditions were selected toperform the optimization analysis. Buffalo’s climate conditionrequires space heating for majority of the year (550 cooling-

4 Copyright c© 2012 by ASME

degree-days), so Buffalo was assumed a heating dominated citywith eight months heating (starting on October). To simplify theintroduction of the weather data, the air and dew-pint tempera-ture, and solar radiation values for Buffalo were introduced to themodel via estimation of these inputs by cosine functions (Eqns.5, 6, and 7). Weather data were obtained form the NationalOperational Hydrologic Remote Sensing Center (NOHRSC) andcurve-fitting was undertaken in Microsoft Excel environment toobtain the input parameters to the cosine models. A summary ofthe input parameters to the model is presented in Table 3. An-nual variations of ambient air temperature and solar radiation forBuffalo region are depicted in Figures 3 and 4, respectively.

TABLE 3. Weather data for Buffalo as input to the model

City Buffalo

Ta−avg (◦C) 9.3

Ta−amp (◦C) 14.1

αair (month) 10.1

Tdew−avg (◦C) 4.3

Tdew−amp (◦C) 12.2

αdew (month) 10.2

Qsi−avg (Wm−2) 142

Qsi−amp (Wm−2) 75

αQsi (month) 9.2

Us (ms−1) 4.2

Tair = Ta−avg +Ta−amp cos(2πtP− αair

12) (5)

where Ta−avg is the average annual air temperature, Ta−amp isthe amplitude of fluctuation of annual air temperature, and αairis the fitted cosine model phase difference for air temperature,calculated based on the start time of the modeling on Octoberfirst (the assumed heating season start time).

Tdewpoint = Tdew−avg +Tdew−amp cos(2πtP− αdew

12) (6)

where Tdew−avg is the average annual dew-point temperature,Tdew−amp is the amplitude of annual dew-point temperature vari-

ation, and αdew is the fitted cosine model phase difference fordew-point temperature.

Qsi = Qsi−avg +Qsi−amp cos(2πtP−

αQsi

12) (7)

where Qsi−avg is the average annual solar radiation reachingthe surface, Qsi−amp is the amplitude of fluctuation of the solarradiation throughout the year, and the αQsi is the fitted cosinemodel phase difference for radiation.

0 50 100 150 200 250 300 350−5

0

5

10

15

20

25

Days (starting on October 1st)

Air

tem

pera

ture

(D

eg

rees C

)

Buffalo

FIGURE 3. Annual Buffalo air temperature variation

0 50 100 150 200 250 300 35060

80

100

120

140

160

180

200

220

Days (starting on October 1st)

So

lar

rad

iati

on

(W

/m2)

Buffalo

FIGURE 4. Annual Buffalo solar radiation variation

A genetic algorithm (GA) optimization scheme was de-signed to obtain the optimum values of the intermediate layer

5 Copyright c© 2012 by ASME

configuration (thickness and position), working fluid type, andinlet fluid temperature ranges. The results of the simulation willprovide insights on the benefits returned from the introductionof an intermediate layer on the performance of a GSHP through-out the year. The model outputs will clarify what are the opti-mal properties and configuration of the intermediate layer andwhat are the optimal entering water temperatures to the groundthroughout the year to achieve the maximal efficiency. The ulti-mate motivation behind the analysis is to determine whether theoperating parameters for different months of the year are in arange that confirms the benefits of utilization of a capacity con-trol strategy or a set of selected operational parameters can beused for all months without deviating from the optimized condi-tion.

Seven input variables (decision variables) were chosen tofeed the core finite difference model in each run of the GA.These inputs comprise working fluid properties, minimum, meanand maximum entering water temperature values, the intermedi-ate layer thickness, position and thermal properties. To let theevolutionary algorithm search in a broader spectrum of potentialanswers, the algorithm was allowed to choose between a rangeof practical working fluid properties (Table 4, extracted from theIGSHPA guideline [1]) and also a range of common soil prop-erties in ground heat pumps (Table 5, extracted from the man-ual for the soil and rock classification for design of ground heatpumps [21]).

The values of heat extraction/dissipation rates were calcu-lated based on Eqn. (8) for each run after selection of theseparameters. The time averaged values of outlet (T avg

out ) and in-let (T avg

in ) working fluid temperatures for the simulation period(monthly or seasonal) are used to calculate energy extraction/dis-sipation rates in heating/cooling modes. The heat pump workwas calculated based on the friction factor and Reynolds numberdescribed in [30]. The objective function used in the simulationis equal to the reciprocal of difference between the energy extrac-tion rate and the circulating pump energy consumption rate. Asingle-objective genetic algorithm model was used subsequentlyto search for the parameters that minimize the objective functionor, in other words, maximize the net energy extraction/dissipa-tion rates. It was assumed that maximum heating/cooling energydemand from the ground pipes does not exceed 1.5 Kw in anyof the cities while the optimization was aimed towards maximiz-ing the extraction/dissipation rates. A population size of 500 and50 generation was selected for each run of the model. A totalnumber of 32 processors of 48 GB memory were used for eachmonthly run of the model which resulted in runtime of about 7hours. A total number of 64 processors of 48 GB memory wereused for each annual run which resulted in the runtime of about65 hours for each year.

Energy = m ·Cp, f · (T avgout −T avg

in ) (8)

TABLE 5. Intermediate layer thermal properties

Index Intermediate Thermal Thermal

number material conductivity diffusivity

(Wm−1◦C−1) (cm2s−1)10−8

1 TDA 0.29 58

2 Sand 0.77 45

3 Clay 1.11 54

4 Loam 0.91 49

5 Saturated silt or clay 1.67 66

6 Saturated sand 2.5 93

RESULTS AND DISCUSSIONThe main motive behind doing the optimization separately

for each month was to have an estimate of what the range ofthe optimal operating parameters are and how the optimal pa-rameters vary from month to month. The selection of the in-termediate layer material type by the algorithm assures a non-homogeneous profile that provides the optimum performance.The optimized monthly parameters for Buffalo are presented inTable 6. Columns in Table 6 contain the optimized values ofthe variables introduced to the algorithm. Columns 2-5 containentering water parameters where columns 6-8 contain the inter-mediate layer parameters. The last two columns contain the en-ergy extraction rates from the ground with the parameter valuesspecified in other columns plus the percent difference betweenthe calculated energy rates and the corresponding values for thehomogeneous (non TDA) soil profile. Values of the minimum(Tf ,imin ) and average (Tf ,imean ) monthly entering water tempera-tures to the ground are presented in columns 3 and 5 for the firsteight heating months whereas the maximum ( Tf ,imax ) and aver-age (Tf ,imean ) entering water temperatures to the pipe in the fourcooling months are in columns 4 and 5. Based on the results,TDA was selected as the dominant intermediate layer for eightmonths of the year of which five months are in heating season.In the cooling season, TDA was not selected only in July wherethe saturated sand tends to exhibit a dominant effect as an inter-mediate layer.

6 Copyright c© 2012 by ASME

TABLE 4. Working fluid properties

Index Fluid Thermal Specific heat Density Dynamic

number conductivity viscosity

(Wm−1◦C−1) (JKg−1◦C−1) (Kgm−3) (Kgm−1s−1) ·10−3

1 Water 0.6 4183 998.3 1

2 6 % Propylene-glycol and water 0.476 4140 1010 1.5

3 13 % Propylene-glycol and water 0.432 4100 1010 1.9

4 18 % Propylene-glycol and water 0.408 4060 1020 3

5 24 % Propylene-glycol and water 0.389 4020 1020 6.3

TABLE 6. Genetic Algorithm simulation results for each month of the design year

Month Fluid Tf ,imin Tf ,imax Tf ,imean Thickness Position Intermediate Energy %

(index) (◦C) (◦C) (◦C) (m) (m) material (Watt)

Oct 3 4.0 5.2 0.4 1.4 Saturated sand 558 6.5

Nov 1 -3.0 5.2 0.7 0.6 TDA 492 4.7

Dec 2 -3.0 5.5 0.9 0.5 TDA 515 13.2

Jan 2 -3.0 8.0 1.0 0.5 TDA 474 18.8

Feb 2 -3.0 6.8 1.0 0.7 TDA 375 19.4

Mar 1 -3.0 5.2 0.5 1.0 TDA 252 6.8

Apr 3 -2.6 5.2 0.8 1.0 Saturated sand 179 9.1

May 3 4.7 5.2 0.4 1.3 Saturated sand 295 11.7

Jun 1 40.4 35.8 0.5 0.5 TDA 1500 2.5

Jul 2 42.4 32.5 0.3 1.4 Saturated sand 1500 1.2

Aug 4 43.0 41.6 0.3 1.0 TDA 1500 1.2

Sep 4 45.6 34.5 0.4 0.5 TDA 1500 1.3

Avg. Heating -1 5.9 0.7 0.8 393

Avg. Cooling 42.8 36.1 0.4 0.8 1500

One interesting observation from the monthly results is thatonly the two intermediate materials with lowest and highest ther-mal conductivities were selected as the choice blanket materialthrough the optimization algorithm. The fact that TDA (the leastconductive) or saturated sand (the most conductive) were se-lected among the provided list of intermediate material suggeststhat the ground pipes can benefit from a blanket above the pipeburial depth throughout the year but not necessarily from the one

with highest insulation properties.

It should be noted that this modeling procedure does nottake into account other characteristics of either of the TDA orsaturated sand materials. Characteristics such as the porosityand water holding capacity which might potentially contribute toconsiderably different performance results than that only basedon the heat conduction in the soil medium. TDA’s porous struc-ture can potentially enhance the moisture migration to the un-

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TABLE 7. Input parameters from Genetic Algorithm simulation to obtain optimal annual energy rates

Tf ,imin Tf ,imean−heating Thickness Position Tf ,imax Tf ,imean−cooling

(◦C) (◦C) (m) (m) (◦C) (◦C)

-2.3 7.0 0.5 0.3 45.7 31.7

TDA energy Clay energy Homog. energy Mode % TDA % Clay

(Watt) (Watt) (Watt)

324 274 280 Heating 15.7 -2.1

1497 1375 1391 Cooling 7.6 -1.2

derlying layers of soil where higher moisture can contribute tohigher thermal conductivities of the soil around the pipes. It isexpected that this characteristics of TDA has a more substantialeffect in the summer time specially in regions with less rainfallevents. Authors are currently in the process of installation ofa field experiment to compare TDA blanket to a conventionalbackfill control section to verify other potential enhancementson GSHP performance by utilization of the TDA layer.

The values in the last column in Table 6 represent the per-centage energy extraction/dissipation rate increase compared tothe homogenous soil profile in different months. The percent-ages increase in energy extraction rate for the coldest months inBuffalo (Jan-Feb) are as high as 18-19%. This finding can betranslated in the potential for higher efficiency achievement witha non-homogeneous soil profile in heating season in Buffalo.

Based on the results, relatively small efficiency improve-ments are achievable with the non-homogeneous soil profile forcooling purposes in warmer months of the year. It should benoted that the performance of the homogeneous system is veryclose to the maximum expected cooling capacity (1500 Watts)in warmest months of the year which contributes to the marginalimprovements with a non-homogeneous soil profile. If the de-signed ground pipe network was supposed to provide higherenergy rates, the percentage increase in energy extraction rateswould probably be higher and more pronounced with the non-homogeneous soil profile.

After performing the analysis for each month and gainingadequate knowledge of the relationship between selected vari-ables, the next step of the modeling focused on finding the op-timal values of the inlet fluid temperature and the configurationof the intermediate layer for year-round optimization. Knowingwhat the two dominant choices of the algorithm for the interme-diate blanket are, separate runs of the model with TDA were exe-cuted to compare the results with the homogeneous and saturatedsand soil profiles. The working fluid properties were set to 13%Propylene-glycol and water mixture for the annual runs. Theexisting knowledge of the system from monthly results helpedconstructing a more efficient optimization scheme with less vari-

ables for the annual runs. The goal for this step was to obtainthe optimal values of temperatures and intermediate layer con-figuration by optimizing the energy extraction/dissipation for thewhole year. This approach provides the designer with the opti-mized working fluid temperatures and intermediate layer’s con-figuration which yields the best performance.

The results of the annual optimization are presented in Ta-ble 7. The percentages in the Table 7 refer to the percent increasein the energy extraction/dissipation rates compared to the homo-geneous soil profile. The obtained optimized temperature val-ues and intermediate layer configuration for TDA were used torun the homogeneous and saturated sand scenarios for the sakeof comparison. The algorithm yields higher maximum and av-erage temperature values for heating season where compared toaverage monthly values (Table 6) to obtain the maximum perfor-mance over the entire season. The optimized annual TDA thick-ness and positions are relatively smaller than the values obtainedin the monthly optimization. The average annual attainable en-ergy rates are also comparatively lower for the annual run com-pared to monthly values.

The values of the annual energy rates with TDA and satu-rated sand compared to the homogeneous case suggest consid-erably lower annual performance with clay for both heating andcooling season in cold climates as typified by Buffalo (Table 7).TDA exhibits higher performance in heating than cooling (15.7versus 7.6).

The comparison between the optimized annual heating/cool-ing energy rates with the monthly values shows that the opti-mization algorithm yields values that stray from the optimizedmonthly values. This is because it is more challenging for the al-gorithm to find operational parameters which level the differencebetween seasonal energy rates and highest achievable monthlyvalues. This causes the annual energy rates to be relatively lowerthan the ones attainable if the monthly control on the operatingparameters (working fluid temperatures and intermediate layerconfiguration) was an option. These findings suggest that utiliza-tion of control strategies which allow the heat pump’s function-ality with variable working fluid temperatures (or variable refrig-

8 Copyright c© 2012 by ASME

erant flow schemes) in different seasons can improve the overallperformance of the GSHP.

CONCLUSIONSPerformance of a GHSP system with a non-homogeneous

soil profile was examined via GA optimization algorithm. Theevolutionary algorithm was given a range of working fluid prop-erties, intermediate layer thermal properties, a range of operatingtemperatures, and the intermediate layer configuration to searchfor the optimized condition of the system. The optimization pro-cess was performed for Buffalo weather data. A summary offindings are listed below:

X A generic non-homogeneous soil profile showed the poten-tial for significant GSHP performance improvement

X TDA demonstrated higher benefits in colder climates andwith more highlighted impact in heating season.

X TDA demonstrated a marginal enhancement during coolingcycles due to insignificant difference between achievable en-ergy extraction rates with the non-homogeneous and homo-geneous cases.

X The annual optimized energy rates are lower than monthlyvalues suggesting the necessity of a monthly operation con-trol strategy. Aside from the intermediate layer configura-tion which can not be optimized after construction, enter-ing water temperatures to the ground should be controlledvia multi-stage compressors and/or variable speed pumps toachieve the near-optimal GSHP operation.

ACKNOWLEDGMENTPartial funding for this research was provided by Empire

State Developments Environmental Services Unit through theNew York State Tire Derived Aggregate Program at the Uni-versity at Buffalo’s Center for Integrated Waste Management:www.tdanys.buffalo.edu/UB. Authors would like to thank Dr.Keneth Fishman and Mr. Louis Zicari from the Center for In-tegrated Waste Management for their constant support and con-tinuing contribution to this project.

References[1] IGSHPA, 2009. Ground source heat pump residential and

light commercial design and installation guide. Interna-tional Ground Source Heat Pump Association, OklahamaState University, Division of Engineering Technology.

[2] Zogou, O., and Stamatelos, A., 2007. “Optimization ofthermal performance of a building with ground sourceheat pump system”. Energy conversion and management,48(11), pp. 2853–2863.

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